Google Professional Machine Learning Engineer
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Ultimate access to all questions.
Ultimate access to all questions.
You are working as a Machine Learning Engineer and have designed an ML pipeline with multiple input parameters. Your main goal is to evaluate the tradeoffs between different combinations of the following parameters: Input dataset, Max tree depth of the boosted tree regressor, and Optimizer learning rate. To facilitate this, you need to compare the pipeline performance of these different parameter combinations based on F1 score, training time, and model complexity. Additionally, you want your approach to be reproducible and ensure that all pipeline runs are tracked on the same platform. What should you do?
Explanation:
Option D is the correct answer. Vertex AI Experiments and Vertex AI Pipelines provide a structured and trackable environment for investigating parameter tradeoffs. By creating an experiment in Vertex AI Experiments and using a Vertex AI pipeline with a custom model training job, you can configure parameters and submit multiple runs to the same experiment with different values for the parameters. This approach ensures reproducibility, facilitates efficient exploration of various configurations, and allows you to monitor their performance metrics such as F1 score, training time, and model complexity within Vertex AI Experiments.